import torch
import torch.nn as nn
import math

from torch.autograd import Function


class PairwiseDistance(Function):
    def __init__(self, p):
        super(PairwiseDistance, self).__init__()
        self.norm = p

    def forward(self, x1, x2):
        assert x1.size() == x2.size()
        eps = 1e-4 / x1.size(1)
        diff = torch.abs(x1 - x2)
        out = torch.pow(diff, self.norm).sum(dim=1)
        return torch.pow(out + eps, 1. / self.norm)
class TripletMarginLoss(Function):
    """Triplet loss function.
    """
    def __init__(self, margin):
        super(TripletMarginLoss, self).__init__()
        self.margin = margin
        self.pdist = PairwiseDistance(2)  # norm 2

    def forward(self, anchor, positive, negative):
        d_p = self.pdist.forward(anchor, positive)
        d_n = self.pdist.forward(anchor, negative)

        dist_hinge = torch.clamp(self.margin + d_p - d_n, min=0.0)
        loss = torch.mean(dist_hinge)
        return loss


class ReLU(nn.Hardtanh):

    def __init__(self, inplace=False):
        super(ReLU, self).__init__(0, 20, inplace)

    def __repr__(self):
        inplace_str = 'inplace' if self.inplace else ''
        return self.__class__.__name__ + ' (' \
            + inplace_str + ')'


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class myResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):

        super(myResNet, self).__init__()

        self.relu = ReLU(inplace=True)
        self.inplanes = 64
        self.conv1 = nn.Conv2d(1, 64, kernel_size=5, stride=2, padding=2,bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, layers[0])

        self.inplanes = 128
        self.conv2 = nn.Conv2d(64, 128, kernel_size=5, stride=2, padding=2,bias=False)
        self.bn2 = nn.BatchNorm2d(128)
        self.layer2 = self._make_layer(block, 128, layers[1])
        self.inplanes = 256
        self.conv3 = nn.Conv2d(128, 256, kernel_size=5, stride=2, padding=2,bias=False)
        self.bn3 = nn.BatchNorm2d(256)
        self.layer3 = self._make_layer(block, 256, layers[2])
        self.inplanes = 512
        self.conv4 = nn.Conv2d(256, 512, kernel_size=5, stride=2, padding=2,bias=False)
        self.bn4 = nn.BatchNorm2d(512)
        self.layer4 = self._make_layer(block, 512, layers[3])

        
        self.avgpool = nn.AdaptiveAvgPool2d((1,None))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):

        layers = []
        layers.append(block(self.inplanes, planes, stride))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x





class DeepSpeakerModel(nn.Module):
    def __init__(self,embedding_size,num_classes,feature_dim = 64):
        super(DeepSpeakerModel, self).__init__()

        self.embedding_size = embedding_size




        self.model = myResNet(BasicBlock, [1, 1, 1, 1])
        if feature_dim == 64:
            self.model.fc = nn.Linear(512*4, self.embedding_size)
        elif feature_dim == 40:
            self.model.fc = nn.Linear(256 * 5, self.embedding_size)
        self.model.classifier = nn.Linear(self.embedding_size, num_classes)




    def l2_norm(self,input):
        input_size = input.size()
        buffer = torch.pow(input, 2)

        normp = torch.sum(buffer, 1).add_(1e-10)
        norm = torch.sqrt(normp)

        _output = torch.div(input, norm.view(-1, 1).expand_as(input))

        output = _output.view(input_size)

        return output

    def forward(self, x):

        x = self.model.conv1(x)
        x = self.model.bn1(x)
        x = self.model.relu(x)
        x = self.model.layer1(x)

        x = self.model.conv2(x)
        x = self.model.bn2(x)
        x = self.model.relu(x)
        x = self.model.layer2(x)

        x = self.model.conv3(x)
        x = self.model.bn3(x)
        x = self.model.relu(x)
        x = self.model.layer3(x)

        x = self.model.conv4(x)
        x = self.model.bn4(x)
        x = self.model.relu(x)
        x = self.model.layer4(x)

        x = self.model.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.model.fc(x)
        self.features = self.l2_norm(x)
        # Multiply by alpha = 10 as suggested in https://arxiv.org/pdf/1703.09507.pdf
        alpha=10
        self.features = self.features*alpha

        #x = x.resize(int(x.size(0) / 17),17 , 512)
        #self.features =torch.mean(x,dim=1)
        #x = self.model.classifier(self.features)
        return self.features

    def forward_classifier(self, x):
        features = self.forward(x)
        res = self.model.classifier(features)
        return res

